Tag: loss-functions

In machine learning, handling outliers effectively is crucial for building robust models. Traditional loss functions like Mean Squared Error (MSE) can be overly sensitive to outliers, while Mean Absolute Error (MAE) may not provide smooth gradients for optimization. Enter Huber Loss—a powerful alternative that blends the best of both worlds. By switching between squared and…